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Record W2016131516 · doi:10.1139/f09-101

Long-term declines in phosphorus export from forested catchments in south-central Ontario

2009· article· en· W2016131516 on OpenAlexaffvenueabout
M. Catherine Eimers, Shaun A. Watmough, Andrew M. Paterson, Peter J. Dillon, Huaxia Yao

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsTrent UniversityMinistry of the Environment, Conservation and Parks
Fundersnot available
KeywordsEnvironmental scienceSTREAMSSpring (device)Hydrology (agriculture)PhosphorusSeasonalityDrainage basinBayEcologyOceanographyGeographyBiologyGeologyChemistry

Abstract

fetched live from OpenAlex

Total phosphorus (TP) levels in many Canadian Shield lakes in central Ontario have declined over recent decades, despite increases in human activity in most watersheds. To investigate the contribution of changes in catchment export to long-term declines in lake TP, we examined temporal and spatial patterns in TP concentrations and export (1980–1981 to 2001–2002) across 11 subcatchments that drain into three lakes in which average ice-free TP levels have declined by approximately 35%. Annual stream export of TP decreased significantly by 30%–89% in eight of the 11 subcatchments, and decreases in export were driven by declines in TP concentration, not changes in stream flow. Annual average TP concentrations varied fivefold among adjacent subcatchments, and temporal patterns in annual average TP concentrations were poorly correlated. Seasonal patterns of TP concentration were most similar among streams in the spring (March–April–May), and TP export in the spring declined significantly in 10 of the 11 subcatchments. Because spring melt is the principal hydrologic event in these seasonally snow-covered basins, decreases in TP export during the spring were primarily responsible for declines observed in annual export. The drivers of changes in TP over time are unclear at this point but are the focus of current research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.209
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations99
Published2009
Admission routes3
Has abstractyes

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